Junior Forward Deployed Engineer

Sedgwick

. Design, build, and maintain resilient ETL/ELT pipelines from on-premise systems, AWS services, and Azure platforms into Snowflake and downstream AI services .

Posted 10/22/2026full-timeRemote • Florida • United StatesJuniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining ETL/ELT pipelines across multi-cloud environments, with a strong focus on Snowflake architecture and data orchestration. Proficient in Python, SQL, and PySpark, with a solid understanding of machine learning data requirements and generative AI.

Highest-signal resume keywords

Snowflake ArchitectureETL/ELT Pipeline DevelopmentAWS S3, Glue, LambdaPython, SQL, PySpark MasteryData Orchestration Using Airflow

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ETL/ELT Pipeline DevelopmentSnowflake Data ModelingData Quality FrameworksMachine Learning Data NeedsGenerative AI UnderstandingData Extraction TechniquesPerformance TuningFeature EngineeringData WarehousingContainerization Using Docker

Soft Skills

Team CollaborationMulti-Priority ManagementDeadline Orientation

Tools & Technologies

SnowflakeAWS ServicesAzure Data FactoryAirflowDockerVector DatabasesMLOps

Industry Keywords

Data EngineeringCloud ComputingAI ServicesData ScienceData Orchestration

Tech Stack

Tools & technologies

AirflowAWSAzureCloudDockerETLPySparkPythonSQL

About the role

Key responsibilities & impact

Design, build, and maintain resilient ETL/ELT pipelines from on-premise systems, AWS services, and Azure platforms into Snowflake and downstream AI services
Develop and maintain feature stores and analytically optimized datasets for machine learning workflows
Engineer generative AI data pipelines, including extraction, transformation, chunking, and loading into vector databases
Implement Snowflake data modeling, Snowpipe automation, and compute and storage optimization
Execute non-invasive data extraction from legacy systems while preserving system stability
Design and manage cross-platform workflows using Airflow, AWS Step Functions, and Azure Data Factory
Partner with IT, database, infrastructure, and security teams to resolve connectivity and access challenges and secure production approvals
Implement data quality, validation, and observability frameworks
Optimize storage, compute usage, and query performance across Snowflake, AWS, and Azure
Partner with MLOps, Data Science, and AI teams to turn experimental use cases into scalable production data solutions
Perform other duties as assigned

Requirements

What you’ll need

Master’s degree in Computer Science, Data Engineering, or a related field preferred (not required)
Six (6) years of hands-on data engineering experience, with a track record of building production-grade pipelines for Data Science and AI in multi-cloud environments, or equivalent combination of education and experience required
Expert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and integration with external cloud AI services
Advanced hands-on knowledge of AWS S3, Glue, Lambda and Azure Data Factory, Synapse
Mastery of Python, SQL, and PySpark
Deep experience with data orchestration and containerization using Docker
Experience with on-premise SQL, mainframe extracts, and flat files
Understanding of machine learning data needs, including feature engineering
Understanding of generative AI, including vectorization and embedding pipelines
Ability to work in a team environment
Ability to meet or exceed Performance Competencies
Ability to handle multiple priorities and meet deadlines
Travel as required

Benefits

Comp & perks

Work-life balance
Travel as required
Reasonable accommodations when applicable and appropriate
Equal Opportunity Employer
Drug-Free Workplace